1. Field
The present disclosure relates generally to classifying image data and, in particular, to a method and apparatus for clustering data in a point cloud. Still more particularly, the present disclosure relates to a method and apparatus for classifying groups of image data in a point cloud.
2. Background
Manned and unmanned aerial vehicles, ground vehicles, or both may be used to gather data. In particular, vehicles and other platforms may gather data about a scene. Various objects may be present in the scene. This data may take various forms. In some cases, an aerial vehicle may use a visible light camera, an infrared camera, and other cameras to generate image data about the scene. Specifically, the image data may be generated for particular objects in the scene.
In some cases, an aerial vehicle may generate image data for the scene in the form of a point cloud. The point cloud is a set of points in a three-dimensional coordinate system. Each point has a three-dimensional coordinate and may also include other information. The aerial vehicle may use a three-dimensional scanner to generate image data for the point cloud. The three-dimensional scanner may identify points for objects in the scene.
For example, a light detection and ranging (LIDAR) system may be used to generate image data for the point cloud of the scene. In another example, a laser detection and ranging (LADAR) system may be used to generate image data for the scene. This type of system also may be referred to as a light detection and ranging (LIDAR) system. These LIDAR and LADAR may be used interchangeably.
Processing the image data for the point cloud may be more time consuming than desired and also may require more processing power than desired. For example, a point cloud for a scene may include millions of points that form the image data for the point cloud. Processing the point cloud to identify objects in the point cloud may take more time and processing power than desired.
Processing the point cloud to identify objects may not occur as quickly as desired. The time needed to identify objects may be more important when a particular mission involves quickly changing situations. For example, when surveillance is performed on location having one or more targets of interest, identifying moving objects or objects that may potentially move may not occur as quickly as desired in processing a point cloud of the scene.
In another example, if the mission is for fire-fighting in a forest or other location, advancement of flames, locations of fire fighters, and other ground assets may not be identified as quickly as needed to perform a mission using the point cloud. Also, a need for real-time or near-real-time identification of targets of interest and other related information, and more detailed layering of such information is often needed. Despite these needs, known mapping and surveillance systems and capabilities may not enable the speed, resolution, or more detailed layers of information required for ever more demanding applications.
Therefore, it would be desirable to have a method and apparatus that takes into account at least some of the issues discussed above as well as possibly other issues.